Ratel: Interactive Analytics for Large Scale Trajectories

2019 
Trajectory data analytics plays an important role in many applications, such as transportation optimization, urban planning, taxi scheduling, and so on. However, trajectory data analytics has a great challenge that the time cost for processing queries is too high on big datasets. In this paper, we demonstrate a distributed in-memory framework Ratel base on Spark for analyzing large scale trajectories. Ratel groups trajectories into partitions by considering the data locality and load balance. We build R-Tree based global indexes to prune partitions when applying trajectory search and join. For each partition, Ratel uses a filter-refinement method to efficiently find similar trajectories. We show three kinds of scenarios - bus station planning, route recommendation, and transportation analytics. Demo attendees can interact with a web UI, pose different queries on the dataset, and navigate the query result.
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